{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "id": "uudYItMGJel0"
   },
   "source": [
    "# Install Library\n",
    "\n",
    "[RDKit ](https://github.com/rdkit/rdkit)\n",
    "\n",
    "[DGL](https://github.com/dmlc/dgl/)\n",
    "\n",
    "[DGL-LifeSci](https://github.com/awslabs/dgl-lifesci)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "hidden": true,
    "id": "IYZ1ollGWzma",
    "outputId": "2783c8df-d3b8-4606-9611-7ee6e9407627"
   },
   "outputs": [],
   "source": [
    "!pip install --pre dgl -f https://data.dgl.ai/wheels/cu113/repo.html\n",
    "!pip install --pre dglgo -f https://data.dgl.ai/wheels-test/repo.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true,
    "id": "271tuyJSQ-m3"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install rdkit-pypi\n",
    "!pip install dgllife"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "id": "il7EqGqbY5pK"
   },
   "source": [
    "# Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['PYTHONHASHSEED'] = '0'\n",
    "os.environ['CUDA_VISIBLE_DEVICES']='-1'\n",
    "os.environ['TF_CUDNN_USE_AUTOTUNE'] ='0'\n",
    "\n",
    "import dgl \n",
    "import sys\n",
    "import torch\n",
    "import random\n",
    "import statistics\n",
    "import cv2\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "\n",
    "from numpy import array\n",
    "from numpy import argmax\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.callbacks import  History\n",
    "from dgllife.utils import smiles_to_bigraph, CanonicalAtomFeaturizer, AttentiveFPAtomFeaturizer\n",
    "from tqdm.notebook import tqdm, trange\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from utils.general import DATASET, get_dataset, separate_active_and_inactive_data, get_embedding_vector_class, count_lablel,data_generator\n",
    "from utils.gcn_pre_trained import get_tox21_model\n",
    "from model.heterogeneous_siamese_tox21 import siamese_model_attentiveFp_tox21, siamese_model_Canonical_tox21\n",
    "\n",
    "\n",
    "random.seed(1)\n",
    "np.random.seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "id": "1RVgRpTmQ5rp"
   },
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "hidden": true,
    "id": "VunMwXfRM09K",
    "outputId": "66c67adf-0024-4585-81a1-897e7ad4638c"
   },
   "outputs": [],
   "source": [
    "cache_path='./tox21_dglgraph.bin'\n",
    "\n",
    "df = get_dataset(\"tox21\")\n",
    "id = df['mol_id']\n",
    "df = df.drop(columns=['mol_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true,
    "id": "rY_JnWz5M6bU"
   },
   "outputs": [],
   "source": [
    "tox21_tasks = df.columns.values[:12].tolist()\n",
    "tox21_tasks_test = tox21_tasks[9:12]\n",
    "train_task, valid_task= train_test_split(tox21_tasks, test_size=0.2 , shuffle= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "hidden": true,
    "id": "HO5RmBnZM8mq",
    "outputId": "4b0ad369-893f-4246-bbdf-4257b3016f35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NR-AR one: 309  zero: 6956  NAN: 566\n",
      "NR-AR-LBD one: 237  zero: 6521  NAN: 1073\n",
      "NR-AhR one: 768  zero: 5781  NAN: 1282\n",
      "NR-Aromatase one: 300  zero: 5521  NAN: 2010\n",
      "NR-ER one: 793  zero: 5400  NAN: 1638\n",
      "NR-ER-LBD one: 350  zero: 6605  NAN: 876\n",
      "NR-PPAR-gamma one: 186  zero: 6264  NAN: 1381\n",
      "SR-ARE one: 942  zero: 4890  NAN: 1999\n",
      "SR-ATAD5 one: 264  zero: 6808  NAN: 759\n",
      "SR-HSE one: 372  zero: 6095  NAN: 1364\n",
      "SR-MMP one: 918  zero: 4892  NAN: 2021\n",
      "SR-p53 one: 423  zero: 6351  NAN: 1057\n"
     ]
    }
   ],
   "source": [
    "one = []\n",
    "zero = []\n",
    "nan = []\n",
    " \n",
    "for task in tox21_tasks:\n",
    "    a = list(df[task].value_counts(dropna=False).to_dict().values())\n",
    "    zero.append(a[0])\n",
    "    nan.append(a[1])\n",
    "    one.append(a[2])\n",
    "    print(task ,\"one:\" ,a[2] ,\" zero:\", a[0], \" NAN:\",a[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 894
    },
    "hidden": true,
    "id": "ZZcv6iPNNS19",
    "outputId": "6c6f9597-0181-4b83-8e31-943902aedb01"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing the matplotlib library\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# Declaring the figure or the plot (y, x) or (width, height)\n",
    "plt.figure(figsize=[20, 15])\n",
    "\n",
    "X = np.arange(1,len(tox21_tasks)+1)\n",
    "plt.bar(X + 0.2, one, color = 'g', width = 0.25)\n",
    "plt.bar(X + 0.4, zero, color = 'b', width = 0.25)\n",
    "plt.bar(X + 0.6, nan, color = 'r', width = 0.25)\n",
    "\n",
    "# Creating the legend of the bars in the plot\n",
    "plt.legend(['Active' , 'Inactive' ,'NAN'])\n",
    "# Overiding the x axis with the country names\n",
    "plt.xticks([i + 0.25 for i in range(1,13)], X)\n",
    "# Giving the tilte for the plot\n",
    "plt.title(\"Tox21 dataset diagram\")\n",
    "# Namimg the x and y axis\n",
    "plt.xlabel('Tox21_tasks')\n",
    "plt.ylabel('Cases')\n",
    "# Saving the plot as a 'png'\n",
    "plt.savefig('4BarPlot.png')\n",
    "# Displaying the bar plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "id": "6grIE_JeqkUZ"
   },
   "source": [
    "# Required functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hidden": true,
    "id": "m9K0ArLh6BC1"
   },
   "outputs": [],
   "source": [
    "def create_dataset_with_gcn(ds, class_embed_vector, GCN, tasks):\n",
    "\n",
    "    created_data = []\n",
    "    data = np.arange(len(tasks))\n",
    "    onehot_encoded = to_categorical(data)\n",
    "    for numberTask, dataset in enumerate(ds):\n",
    "        for i, data in enumerate(dataset):\n",
    "            smile, g, label, mask = data\n",
    "            g = g.to(device)\n",
    "            g = dgl.add_self_loop(g)\n",
    "            graph_feats = g.ndata.pop('h')\n",
    "            embbed = GCN(g, graph_feats)\n",
    "            embbed = embbed.to('cpu')\n",
    "            embbed = embbed.detach().numpy()\n",
    "            a = (smiles, embbed, onehot_encoded[numberTask], class_embed_vector[numberTask], label, tasks[numberTask])\n",
    "            created_data.append(a)\n",
    "    print('Data created!!')\n",
    "    return created_data \n",
    "\n",
    "\n",
    "def create_meta_data_gcn(GCN, dataset, class_embed_vector, train_task_size, tasks):\n",
    "    \n",
    "    train_data= []\n",
    "    valid_data = []\n",
    "    data = np.arange(len(tasks))\n",
    "    onehot_encoded = to_categorical(data)\n",
    "    \n",
    "    for numberTask , datas in enumerate(dataset):\n",
    "        \n",
    "        for j, data in enumerate(datas):\n",
    "            \n",
    "            smiles, g, label, mask = data\n",
    "            \n",
    "            g = g.to(device)\n",
    "            g = dgl.add_self_loop(g)\n",
    "            graph_feats = g.ndata.pop('h')\n",
    "            embbed = GCN(g, graph_feats)\n",
    "            embbed = embbed.to('cpu')\n",
    "            embbed = embbed.detach().numpy()\n",
    "            a = (smiles, embbed, onehot_encoded[numberTask], class_embed_vector[numberTask], label[0], tasks[numberTask])\n",
    "\n",
    "            \n",
    "            if numberTask < train_task_size :\n",
    "                train_data.append(a)\n",
    "            else:\n",
    "                valid_data.append(a)\n",
    "\n",
    "    print('Data created!!')            \n",
    "    return train_data, valid_data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Calculation of embedded vectors for each class "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NR-AR=> positive: 309 - negative: 6956\n",
      "NR-AR-LBD=> positive: 237 - negative: 6521\n",
      "NR-AhR=> positive: 768 - negative: 5781\n",
      "NR-Aromatase=> positive: 300 - negative: 5521\n",
      "NR-ER=> positive: 793 - negative: 5400\n",
      "NR-ER-LBD=> positive: 350 - negative: 6605\n",
      "NR-PPAR-gamma=> positive: 186 - negative: 6264\n",
      "SR-ARE=> positive: 942 - negative: 4890\n",
      "SR-ATAD5=> positive: 264 - negative: 6808\n",
      "SR-HSE=> positive: 372 - negative: 6095\n",
      "SR-MMP=> positive: 918 - negative: 4892\n",
      "SR-p53=> positive: 423 - negative: 6351\n"
     ]
    }
   ],
   "source": [
    "df_positive, df_negative = separate_active_and_inactive_data (df, tox21_tasks)\n",
    "\n",
    "for i,d in enumerate(zip(df_positive,df_negative)):\n",
    "    print(f'{tox21_tasks[i]}=> positive: {len(d[0])} - negative: {len(d[1])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dgl graphs from scratch...\n",
      "Processing dgl graphs from scratch...\n",
      "Processing dgl graphs from scratch...\n",
      "Processing dgl graphs from scratch...\n",
      "Processing dgl graphs from scratch...\n",
      "Processing dgl graphs from scratch...\n",
      "Processing dgl graphs from scratch...\n",
      "Processing dgl graphs from scratch...\n",
      "Processing dgl graphs from scratch...\n",
      "Processing dgl graphs from scratch...\n",
      "Processing dgl graphs from scratch...\n",
      "Processing dgl graphs from scratch...\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6956\n",
      "Processing molecule 2000/6956\n",
      "Processing molecule 3000/6956\n",
      "Processing molecule 4000/6956\n",
      "Processing molecule 5000/6956\n",
      "Processing molecule 6000/6956\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6521\n",
      "Processing molecule 2000/6521\n",
      "Processing molecule 3000/6521\n",
      "Processing molecule 4000/6521\n",
      "Processing molecule 5000/6521\n",
      "Processing molecule 6000/6521\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/5781\n",
      "Processing molecule 2000/5781\n",
      "Processing molecule 3000/5781\n",
      "Processing molecule 4000/5781\n",
      "Processing molecule 5000/5781\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/5521\n",
      "Processing molecule 2000/5521\n",
      "Processing molecule 3000/5521\n",
      "Processing molecule 4000/5521\n",
      "Processing molecule 5000/5521\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/5400\n",
      "Processing molecule 2000/5400\n",
      "Processing molecule 3000/5400\n",
      "Processing molecule 4000/5400\n",
      "Processing molecule 5000/5400\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6605\n",
      "Processing molecule 2000/6605\n",
      "Processing molecule 3000/6605\n",
      "Processing molecule 4000/6605\n",
      "Processing molecule 5000/6605\n",
      "Processing molecule 6000/6605\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6264\n",
      "Processing molecule 2000/6264\n",
      "Processing molecule 3000/6264\n",
      "Processing molecule 4000/6264\n",
      "Processing molecule 5000/6264\n",
      "Processing molecule 6000/6264\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/4890\n",
      "Processing molecule 2000/4890\n",
      "Processing molecule 3000/4890\n",
      "Processing molecule 4000/4890\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6808\n",
      "Processing molecule 2000/6808\n",
      "Processing molecule 3000/6808\n",
      "Processing molecule 4000/6808\n",
      "Processing molecule 5000/6808\n",
      "Processing molecule 6000/6808\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6095\n",
      "Processing molecule 2000/6095\n",
      "Processing molecule 3000/6095\n",
      "Processing molecule 4000/6095\n",
      "Processing molecule 5000/6095\n",
      "Processing molecule 6000/6095\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/4892\n",
      "Processing molecule 2000/4892\n",
      "Processing molecule 3000/4892\n",
      "Processing molecule 4000/4892\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6351\n",
      "Processing molecule 2000/6351\n",
      "Processing molecule 3000/6351\n",
      "Processing molecule 4000/6351\n",
      "Processing molecule 5000/6351\n",
      "Processing molecule 6000/6351\n"
     ]
    }
   ],
   "source": [
    "dataset_positive = [DATASET(d, smiles_to_bigraph, AttentiveFPAtomFeaturizer(), cache_file_path = cache_path) for d in df_positive]\n",
    "dataset_negative = [DATASET(d, smiles_to_bigraph, AttentiveFPAtomFeaturizer(), cache_file_path = cache_path) for d in df_negative]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class vector created!!\n"
     ]
    }
   ],
   "source": [
    "embed_class_tox21 = get_embedding_vector_class(dataset_positive, dataset_negative, radius=2, size = 512)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DQuTm_lmlOOM"
   },
   "source": [
    "# Meta-Learning with BioAct-Het and AttentiveFP GCN "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "id": "LIx0yuwm6D42"
   },
   "source": [
    "### Siamea model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading GCN_attentivefp_Tox21_pre_trained.pth from https://data.dgl.ai/dgllife/pre_trained/gcn_attentivefp_tox21.pth...\n",
      "Pretrained model loaded\n"
     ]
    }
   ],
   "source": [
    "#  Attentive FP GCN\n",
    "model_name = 'GCN_attentivefp_Tox21'\n",
    "gcn_model = get_tox21_model(model_name)\n",
    "gcn_model.eval()\n",
    "gcn_model = gcn_model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/7265\n",
      "Processing molecule 2000/7265\n",
      "Processing molecule 3000/7265\n",
      "Processing molecule 4000/7265\n",
      "Processing molecule 5000/7265\n",
      "Processing molecule 6000/7265\n",
      "Processing molecule 7000/7265\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6758\n",
      "Processing molecule 2000/6758\n",
      "Processing molecule 3000/6758\n",
      "Processing molecule 4000/6758\n",
      "Processing molecule 5000/6758\n",
      "Processing molecule 6000/6758\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6549\n",
      "Processing molecule 2000/6549\n",
      "Processing molecule 3000/6549\n",
      "Processing molecule 4000/6549\n",
      "Processing molecule 5000/6549\n",
      "Processing molecule 6000/6549\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/5821\n",
      "Processing molecule 2000/5821\n",
      "Processing molecule 3000/5821\n",
      "Processing molecule 4000/5821\n",
      "Processing molecule 5000/5821\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6193\n",
      "Processing molecule 2000/6193\n",
      "Processing molecule 3000/6193\n",
      "Processing molecule 4000/6193\n",
      "Processing molecule 5000/6193\n",
      "Processing molecule 6000/6193\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6955\n",
      "Processing molecule 2000/6955\n",
      "Processing molecule 3000/6955\n",
      "Processing molecule 4000/6955\n",
      "Processing molecule 5000/6955\n",
      "Processing molecule 6000/6955\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6450\n",
      "Processing molecule 2000/6450\n",
      "Processing molecule 3000/6450\n",
      "Processing molecule 4000/6450\n",
      "Processing molecule 5000/6450\n",
      "Processing molecule 6000/6450\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/5832\n",
      "Processing molecule 2000/5832\n",
      "Processing molecule 3000/5832\n",
      "Processing molecule 4000/5832\n",
      "Processing molecule 5000/5832\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/7072\n",
      "Processing molecule 2000/7072\n",
      "Processing molecule 3000/7072\n",
      "Processing molecule 4000/7072\n",
      "Processing molecule 5000/7072\n",
      "Processing molecule 6000/7072\n",
      "Processing molecule 7000/7072\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6467\n",
      "Processing molecule 2000/6467\n",
      "Processing molecule 3000/6467\n",
      "Processing molecule 4000/6467\n",
      "Processing molecule 5000/6467\n",
      "Processing molecule 6000/6467\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/5810\n",
      "Processing molecule 2000/5810\n",
      "Processing molecule 3000/5810\n",
      "Processing molecule 4000/5810\n",
      "Processing molecule 5000/5810\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6774\n",
      "Processing molecule 2000/6774\n",
      "Processing molecule 3000/6774\n",
      "Processing molecule 4000/6774\n",
      "Processing molecule 5000/6774\n",
      "Processing molecule 6000/6774\n"
     ]
    }
   ],
   "source": [
    "data_ds = []\n",
    "for i, task in  enumerate(tox21_tasks):\n",
    "    a = df[['smiles' , task]]\n",
    "    a = a.dropna()\n",
    "    ds = DATASET(a, smiles_to_bigraph, AttentiveFPAtomFeaturizer(), cache_file_path = cache_path)\n",
    "    data_ds.append(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "hidden": true,
    "id": "HGiScGzpzl0h",
    "outputId": "3e682170-5ae9-4a77-c38f-39a61968107b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data created!!\n"
     ]
    }
   ],
   "source": [
    "train_dataset, valid_dataset = create_meta_data_gcn (gcn_model, data_ds, embed_class_tox21, 9, tox21_tasks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "hidden": true,
    "id": "hn2k_msYf9Rl",
    "outputId": "095e992a-3e82-4eb6-9b57-4ddcf083b2b2",
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58895-19051\n"
     ]
    }
   ],
   "source": [
    "print(f'{len(train_dataset)}-{len(valid_dataset)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "hidden": true,
    "id": "Cuw4bJZZgOoW",
    "outputId": "324218d4-9155-4a08-a4e4-5208e2cfdf9c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train positive label: 4149 - train negative label: 54746\n",
      "Test positive label: 1713 - Test negative label: 17338\n"
     ]
    }
   ],
   "source": [
    "label_pos , label_neg, _ , _  = count_lablel(train_dataset)\n",
    "print(f'train positive label: {label_pos} - train negative label: {label_neg}')\n",
    "\n",
    "label_pos , label_neg, _ , _ = count_lablel(valid_dataset)\n",
    "print(f'Test positive label: {label_pos} - Test negative label: {label_neg}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "hidden": true,
    "id": "fznYoXwWcM9O",
    "outputId": "f08e2441-45bd-4803-b4c6-e91c901d41bd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siamese_net = siamese_model_attentiveFp_tox21()\n",
    "\n",
    "plot_model(siamese_net, show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "id": "ZUo6iyu_Hwk4"
   },
   "source": [
    "### Training algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "hidden": true,
    "id": "UvKopfaM1yAP",
    "outputId": "b05614c9-0f35-49c8-f53a-142198d775db",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.2381 - accuracy: 0.9274 - mae: 0.1303 - mse: 0.0626 - auc_2: 0.7583\n",
      "Epoch 2/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.2111 - accuracy: 0.9295 - mae: 0.1163 - mse: 0.0564 - auc_2: 0.8096\n",
      "Epoch 3/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.2033 - accuracy: 0.9297 - mae: 0.1128 - mse: 0.0549 - auc_2: 0.8254\n",
      "Epoch 4/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.2002 - accuracy: 0.9302 - mae: 0.1107 - mse: 0.0542 - auc_2: 0.8302\n",
      "Epoch 5/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1960 - accuracy: 0.9326 - mae: 0.1078 - mse: 0.0530 - auc_2: 0.8366\n",
      "Epoch 6/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1937 - accuracy: 0.9335 - mae: 0.1066 - mse: 0.0526 - auc_2: 0.8413\n",
      "Epoch 7/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1907 - accuracy: 0.9342 - mae: 0.1049 - mse: 0.0518 - auc_2: 0.8480\n",
      "Epoch 8/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1894 - accuracy: 0.9344 - mae: 0.1039 - mse: 0.0514 - auc_2: 0.8503\n",
      "Epoch 9/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1888 - accuracy: 0.9339 - mae: 0.1043 - mse: 0.0516 - auc_2: 0.8531\n",
      "Epoch 10/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1862 - accuracy: 0.9359 - mae: 0.1028 - mse: 0.0506 - auc_2: 0.8554\n",
      "Epoch 11/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1866 - accuracy: 0.9351 - mae: 0.1023 - mse: 0.0508 - auc_2: 0.8562\n",
      "Epoch 12/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1843 - accuracy: 0.9364 - mae: 0.1015 - mse: 0.0503 - auc_2: 0.8612\n",
      "Epoch 13/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1826 - accuracy: 0.9368 - mae: 0.1006 - mse: 0.0497 - auc_2: 0.8625\n",
      "Epoch 14/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1815 - accuracy: 0.9371 - mae: 0.0991 - mse: 0.0495 - auc_2: 0.8645\n",
      "Epoch 15/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1802 - accuracy: 0.9384 - mae: 0.0983 - mse: 0.0489 - auc_2: 0.8650\n",
      "93\n",
      "93\n",
      "93\n",
      "93\n",
      "93\n",
      "93\n",
      "93\n",
      "93\n",
      "93\n",
      "93\n",
      "93\n",
      "92\n",
      "92\n",
      "92\n",
      "Epoch 1/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1794 - accuracy: 0.9387 - mae: 0.0980 - mse: 0.0486 - auc_2: 0.8654\n",
      "Epoch 2/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1794 - accuracy: 0.9385 - mae: 0.0977 - mse: 0.0487 - auc_2: 0.8666\n",
      "Epoch 3/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1775 - accuracy: 0.9386 - mae: 0.0975 - mse: 0.0484 - auc_2: 0.8703\n",
      "Epoch 4/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1773 - accuracy: 0.9394 - mae: 0.0973 - mse: 0.0483 - auc_2: 0.8704\n",
      "Epoch 5/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1748 - accuracy: 0.9393 - mae: 0.0961 - mse: 0.0478 - auc_2: 0.8759\n",
      "Epoch 6/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1745 - accuracy: 0.9387 - mae: 0.0953 - mse: 0.0477 - auc_2: 0.8766\n",
      "Epoch 7/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1745 - accuracy: 0.9392 - mae: 0.0956 - mse: 0.0477 - auc_2: 0.8770\n",
      "Epoch 8/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1726 - accuracy: 0.9407 - mae: 0.0940 - mse: 0.0470 - auc_2: 0.8798\n",
      "Epoch 9/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1728 - accuracy: 0.9403 - mae: 0.0946 - mse: 0.0472 - auc_2: 0.8795\n",
      "Epoch 10/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1709 - accuracy: 0.9412 - mae: 0.0943 - mse: 0.0466 - auc_2: 0.8825\n",
      "Epoch 11/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1704 - accuracy: 0.9408 - mae: 0.0927 - mse: 0.0465 - auc_2: 0.8834\n",
      "Epoch 12/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1691 - accuracy: 0.9415 - mae: 0.0925 - mse: 0.0462 - auc_2: 0.8845\n",
      "Epoch 13/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1684 - accuracy: 0.9418 - mae: 0.0923 - mse: 0.0459 - auc_2: 0.8865\n",
      "Epoch 14/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1678 - accuracy: 0.9414 - mae: 0.0922 - mse: 0.0459 - auc_2: 0.8872\n",
      "Epoch 15/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1675 - accuracy: 0.9417 - mae: 0.0914 - mse: 0.0458 - auc_2: 0.8885\n",
      "94\n",
      "94\n",
      "94\n",
      "94\n",
      "94\n",
      "94\n",
      "94\n",
      "93\n",
      "93\n",
      "93\n",
      "93\n",
      "93\n",
      "93\n",
      "93\n",
      "Epoch 1/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1661 - accuracy: 0.9407 - mae: 0.0916 - mse: 0.0457 - auc_2: 0.8920\n",
      "Epoch 2/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1670 - accuracy: 0.9415 - mae: 0.0913 - mse: 0.0456 - auc_2: 0.8887\n",
      "Epoch 3/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1636 - accuracy: 0.9422 - mae: 0.0896 - mse: 0.0447 - auc_2: 0.8936\n",
      "Epoch 4/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1641 - accuracy: 0.9424 - mae: 0.0898 - mse: 0.0449 - auc_2: 0.8944\n",
      "Epoch 5/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1613 - accuracy: 0.9435 - mae: 0.0893 - mse: 0.0442 - auc_2: 0.8982\n",
      "Epoch 6/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1621 - accuracy: 0.9431 - mae: 0.0888 - mse: 0.0446 - auc_2: 0.8981\n",
      "Epoch 7/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1608 - accuracy: 0.9434 - mae: 0.0881 - mse: 0.0441 - auc_2: 0.8991\n",
      "Epoch 8/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1609 - accuracy: 0.9438 - mae: 0.0888 - mse: 0.0440 - auc_2: 0.8982\n",
      "Epoch 9/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1596 - accuracy: 0.9448 - mae: 0.0873 - mse: 0.0437 - auc_2: 0.9010\n",
      "Epoch 10/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1602 - accuracy: 0.9434 - mae: 0.0890 - mse: 0.0440 - auc_2: 0.9011\n",
      "Epoch 11/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1585 - accuracy: 0.9447 - mae: 0.0866 - mse: 0.0434 - auc_2: 0.9012\n",
      "Epoch 12/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1571 - accuracy: 0.9455 - mae: 0.0861 - mse: 0.0431 - auc_2: 0.9037\n",
      "Epoch 13/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1558 - accuracy: 0.9453 - mae: 0.0859 - mse: 0.0427 - auc_2: 0.9054\n",
      "Epoch 14/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1565 - accuracy: 0.9449 - mae: 0.0864 - mse: 0.0430 - auc_2: 0.9053\n",
      "Epoch 15/15\n",
      "461/461 [==============================] - 2s 5ms/step - loss: 0.1537 - accuracy: 0.9457 - mae: 0.0845 - mse: 0.0423 - auc_2: 0.9092\n",
      "94\n",
      "94\n",
      "94\n",
      "94\n",
      "94\n",
      "94\n",
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      "94\n",
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      "94\n",
      "94\n",
      "94\n",
      "94\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# models = [siamese_net for task in sider_tasks_final]\n",
    "Epoch_S = 15\n",
    "l = []\n",
    "r = []\n",
    "lbls = []\n",
    "for i , data in enumerate(train_dataset):\n",
    "    smile, embbed_drug, onehot_task, embbed_task, lbl, task_name = data\n",
    "    l.append(embbed_drug[0])\n",
    "    r.append(embbed_task)\n",
    "    lbls.append(lbl.tolist())\n",
    "\n",
    "l = np.array(l).reshape(-1,512,1)\n",
    "r = np.array(r).reshape(-1,512,1)\n",
    "lbls=np.array(lbls)\n",
    "\n",
    "history = History()\n",
    "\n",
    "P = siamese_net.fit([l, r], lbls, epochs = Epoch_S, shuffle=True, batch_size=128, callbacks=[history])\n",
    "\n",
    "for j in range(1000):\n",
    "    C=1\n",
    "    Before = int(P.history['accuracy'][-1]*100)\n",
    "    for i in range(2,Epoch_S+1):\n",
    "        if  int(P.history['accuracy'][-i]*100)== Before:\n",
    "            C=C+1\n",
    "        else:\n",
    "            C=1\n",
    "        Before=int(P.history['accuracy'][-i]*100)\n",
    "        print(Before)\n",
    "    if C==Epoch_S:\n",
    "        break\n",
    "    P = siamese_net.fit([l, r], lbls, epochs = Epoch_S, shuffle=True, batch_size=128, callbacks=history)\n",
    "print(j+1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "id": "2c_4DEdVIBw4"
   },
   "source": [
    "### Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "hidden": true,
    "id": "b-WGsaHvlL8Q"
   },
   "outputs": [],
   "source": [
    "valid_ds = {}\n",
    "\n",
    "for i, task in enumerate(tox21_tasks_test):\n",
    "    temp = []\n",
    "    for j , data in enumerate(valid_dataset):\n",
    "        smiles, embbed_drug, onehot_task, embbed_task, lbl, task_name = data\n",
    "        if task ==  task_name:\n",
    "            temp.append(data)\n",
    "    \n",
    "    valid_ds[task] = temp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SR-HSE : \n",
      "203/203 [==============================] - 0s 680us/step - loss: 0.1902 - accuracy: 0.9436 - mae: 0.1187 - mse: 0.0497 - auc_2: 0.8065\n",
      "SR-MMP : \n",
      "182/182 [==============================] - 0s 638us/step - loss: 0.3174 - accuracy: 0.8551 - mae: 0.1819 - mse: 0.0990 - auc_2: 0.8642\n",
      "SR-p53 : \n",
      "212/212 [==============================] - 0s 637us/step - loss: 0.1848 - accuracy: 0.9401 - mae: 0.1171 - mse: 0.0503 - auc_2: 0.8498\n"
     ]
    }
   ],
   "source": [
    "task_scores = [tox21_tasks_test for tox21_tasks_test in range(len(tox21_tasks_test))]\n",
    "\n",
    "for i, task in enumerate(tox21_tasks_test):\n",
    "    print(task,\": \")\n",
    "\n",
    "    y_test = []\n",
    "    l_val = []\n",
    "    r_val = []\n",
    "    lbls_valid = []\n",
    "    for data in valid_ds[task]:\n",
    "       \n",
    "        smiles, embbed_drug, onehot_task, embbed_task, lbl, task_name = data\n",
    "        l_val.append(embbed_drug[0])\n",
    "        r_val.append(embbed_task)\n",
    "        lbls_valid.append(lbl)\n",
    "            \n",
    "    l1 = np.asarray(l_val)\n",
    "    r1 = np.asarray(r_val)\n",
    "    lbls_valid = np.asarray(lbls_valid)\n",
    "\n",
    "    score = siamese_net.evaluate([l1,r1],lbls_valid)\n",
    " \n",
    "    result =(score[1], score[4])\n",
    "    task_scores[i] = task,result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LB42ZdTCyR-N"
   },
   "source": [
    "# Meta-Learning with BioAct-Het and Canonical GCN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KcaEHmQMcFau"
   },
   "source": [
    "### Siamea model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading GCN_canonical_Tox21_pre_trained.pth from https://data.dgl.ai/dgllife/pre_trained/gcn_canonical_tox21.pth...\n",
      "Pretrained model loaded\n"
     ]
    }
   ],
   "source": [
    "model_GCN = 'GCN_canonical_Tox21'\n",
    "gcn_model = get_tox21_model(model_GCN)\n",
    "gcn_model.eval()\n",
    "gcn_model = gcn_model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/7265\n",
      "Processing molecule 2000/7265\n",
      "Processing molecule 3000/7265\n",
      "Processing molecule 4000/7265\n",
      "Processing molecule 5000/7265\n",
      "Processing molecule 6000/7265\n",
      "Processing molecule 7000/7265\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6758\n",
      "Processing molecule 2000/6758\n",
      "Processing molecule 3000/6758\n",
      "Processing molecule 4000/6758\n",
      "Processing molecule 5000/6758\n",
      "Processing molecule 6000/6758\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6549\n",
      "Processing molecule 2000/6549\n",
      "Processing molecule 3000/6549\n",
      "Processing molecule 4000/6549\n",
      "Processing molecule 5000/6549\n",
      "Processing molecule 6000/6549\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/5821\n",
      "Processing molecule 2000/5821\n",
      "Processing molecule 3000/5821\n",
      "Processing molecule 4000/5821\n",
      "Processing molecule 5000/5821\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6193\n",
      "Processing molecule 2000/6193\n",
      "Processing molecule 3000/6193\n",
      "Processing molecule 4000/6193\n",
      "Processing molecule 5000/6193\n",
      "Processing molecule 6000/6193\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6955\n",
      "Processing molecule 2000/6955\n",
      "Processing molecule 3000/6955\n",
      "Processing molecule 4000/6955\n",
      "Processing molecule 5000/6955\n",
      "Processing molecule 6000/6955\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6450\n",
      "Processing molecule 2000/6450\n",
      "Processing molecule 3000/6450\n",
      "Processing molecule 4000/6450\n",
      "Processing molecule 5000/6450\n",
      "Processing molecule 6000/6450\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/5832\n",
      "Processing molecule 2000/5832\n",
      "Processing molecule 3000/5832\n",
      "Processing molecule 4000/5832\n",
      "Processing molecule 5000/5832\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/7072\n",
      "Processing molecule 2000/7072\n",
      "Processing molecule 3000/7072\n",
      "Processing molecule 4000/7072\n",
      "Processing molecule 5000/7072\n",
      "Processing molecule 6000/7072\n",
      "Processing molecule 7000/7072\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6467\n",
      "Processing molecule 2000/6467\n",
      "Processing molecule 3000/6467\n",
      "Processing molecule 4000/6467\n",
      "Processing molecule 5000/6467\n",
      "Processing molecule 6000/6467\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/5810\n",
      "Processing molecule 2000/5810\n",
      "Processing molecule 3000/5810\n",
      "Processing molecule 4000/5810\n",
      "Processing molecule 5000/5810\n",
      "Processing dgl graphs from scratch...\n",
      "Processing molecule 1000/6774\n",
      "Processing molecule 2000/6774\n",
      "Processing molecule 3000/6774\n",
      "Processing molecule 4000/6774\n",
      "Processing molecule 5000/6774\n",
      "Processing molecule 6000/6774\n"
     ]
    }
   ],
   "source": [
    "data_ds = []\n",
    "for i, task in  enumerate(tox21_tasks):\n",
    "    a = df[['smiles' , task]]\n",
    "    a = a.dropna()\n",
    "    ds = DATASET(a, smiles_to_bigraph, CanonicalAtomFeaturizer(), cache_file_path = cache_path)\n",
    "    data_ds.append(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data created!!\n"
     ]
    }
   ],
   "source": [
    "train_dataset, valid_dataset = create_meta_data_gcn (gcn_model, data_ds, embed_class_tox21, 9, tox21_tasks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train positive label: 4149 - train negative label: 54746\n",
      "Test positive label: 4149 - Test negative label: 54746\n"
     ]
    }
   ],
   "source": [
    "label_pos , label_neg, _ , _ = count_lablel(train_dataset)\n",
    "print(f'train positive label: {label_pos} - train negative label: {label_neg}')\n",
    "\n",
    "label_pos , label_neg, _ , _ = count_lablel(train_dataset)\n",
    "print(f'Test positive label: {label_pos} - Test negative label: {label_neg}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "RfwTLe_8yfrd",
    "outputId": "98d8ff33-0045-4ccf-dbd9-72b1dd46a669"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siamese_net = siamese_model_Canonical_tox21()\n",
    "\n",
    "plot_model(siamese_net, show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "id": "Mi2yDJQVy52B"
   },
   "source": [
    "### Training algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "hidden": true,
    "id": "UWWKNk60y52B",
    "outputId": "1ef42590-8e6c-4af4-964b-7050fc4c4909"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.2525 - accuracy: 0.9287 - mae: 0.1362 - mse: 0.0656 - auc_2: 0.7061\n",
      "Epoch 2/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.2199 - accuracy: 0.9296 - mae: 0.1218 - mse: 0.0590 - auc_2: 0.7873\n",
      "Epoch 3/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.2125 - accuracy: 0.9297 - mae: 0.1179 - mse: 0.0576 - auc_2: 0.8058\n",
      "Epoch 4/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.2103 - accuracy: 0.9296 - mae: 0.1158 - mse: 0.0569 - auc_2: 0.8094\n",
      "Epoch 5/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.2069 - accuracy: 0.9304 - mae: 0.1140 - mse: 0.0561 - auc_2: 0.8160\n",
      "Epoch 6/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.2049 - accuracy: 0.9310 - mae: 0.1117 - mse: 0.0554 - auc_2: 0.8181\n",
      "Epoch 7/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.2033 - accuracy: 0.9309 - mae: 0.1110 - mse: 0.0552 - auc_2: 0.8219\n",
      "Epoch 8/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.2008 - accuracy: 0.9323 - mae: 0.1105 - mse: 0.0545 - auc_2: 0.8275\n",
      "Epoch 9/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1993 - accuracy: 0.9328 - mae: 0.1088 - mse: 0.0542 - auc_2: 0.8307\n",
      "Epoch 10/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1990 - accuracy: 0.9330 - mae: 0.1090 - mse: 0.0538 - auc_2: 0.8312\n",
      "Epoch 11/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1978 - accuracy: 0.9328 - mae: 0.1085 - mse: 0.0537 - auc_2: 0.8332\n",
      "Epoch 12/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1972 - accuracy: 0.9332 - mae: 0.1079 - mse: 0.0535 - auc_2: 0.8353\n",
      "Epoch 13/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1953 - accuracy: 0.9334 - mae: 0.1072 - mse: 0.0530 - auc_2: 0.8391\n",
      "Epoch 14/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1955 - accuracy: 0.9335 - mae: 0.1074 - mse: 0.0530 - auc_2: 0.8383\n",
      "Epoch 15/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1948 - accuracy: 0.9338 - mae: 0.1071 - mse: 0.0528 - auc_2: 0.8388\n",
      "93\n",
      "93\n",
      "93\n",
      "93\n",
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      "93\n",
      "93\n",
      "93\n",
      "92\n",
      "92\n",
      "92\n",
      "92\n",
      "Epoch 1/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1929 - accuracy: 0.9341 - mae: 0.1060 - mse: 0.0527 - auc_2: 0.8444\n",
      "Epoch 2/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1922 - accuracy: 0.9341 - mae: 0.1057 - mse: 0.0524 - auc_2: 0.8456\n",
      "Epoch 3/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1924 - accuracy: 0.9340 - mae: 0.1056 - mse: 0.0524 - auc_2: 0.8446\n",
      "Epoch 4/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1912 - accuracy: 0.9346 - mae: 0.1049 - mse: 0.0519 - auc_2: 0.8462\n",
      "Epoch 5/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1891 - accuracy: 0.9352 - mae: 0.1047 - mse: 0.0517 - auc_2: 0.8517\n",
      "Epoch 6/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1895 - accuracy: 0.9346 - mae: 0.1041 - mse: 0.0517 - auc_2: 0.8501\n",
      "Epoch 7/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1900 - accuracy: 0.9346 - mae: 0.1047 - mse: 0.0518 - auc_2: 0.8488\n",
      "Epoch 8/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1898 - accuracy: 0.9350 - mae: 0.1045 - mse: 0.0518 - auc_2: 0.8502\n",
      "Epoch 9/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1881 - accuracy: 0.9349 - mae: 0.1032 - mse: 0.0515 - auc_2: 0.8536\n",
      "Epoch 10/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1891 - accuracy: 0.9344 - mae: 0.1046 - mse: 0.0518 - auc_2: 0.8515\n",
      "Epoch 11/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1877 - accuracy: 0.9352 - mae: 0.1030 - mse: 0.0511 - auc_2: 0.8520\n",
      "Epoch 12/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1873 - accuracy: 0.9352 - mae: 0.1035 - mse: 0.0511 - auc_2: 0.8550\n",
      "Epoch 13/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1862 - accuracy: 0.9352 - mae: 0.1029 - mse: 0.0509 - auc_2: 0.8574\n",
      "Epoch 14/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1872 - accuracy: 0.9349 - mae: 0.1031 - mse: 0.0512 - auc_2: 0.8551\n",
      "Epoch 15/15\n",
      "461/461 [==============================] - 2s 4ms/step - loss: 0.1871 - accuracy: 0.9356 - mae: 0.1033 - mse: 0.0509 - auc_2: 0.8528\n",
      "93\n",
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      "93\n",
      "93\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# models = [siamese_net for task in sider_tasks_final]\n",
    "Epoch_S = 15\n",
    "l = []\n",
    "r = []\n",
    "lbls = []\n",
    "for i , data in enumerate(train_dataset):\n",
    "    smile, embbed_drug, onehot_task, embbed_task, lbl, task_name = data\n",
    "    l.append(embbed_drug[0])\n",
    "    r.append(embbed_task)\n",
    "    lbls.append(lbl.tolist())\n",
    "\n",
    "l = np.array(l).reshape(-1,128,1)\n",
    "r = np.array(r).reshape(-1,512,1)\n",
    "lbls=np.array(lbls)\n",
    "\n",
    "history = History()\n",
    "\n",
    "P = siamese_net.fit([l, r], lbls, epochs = Epoch_S, shuffle=True, batch_size=128, callbacks=[history])\n",
    "\n",
    "for j in range(1000):\n",
    "    C=1\n",
    "    Before = int(P.history['accuracy'][-1]*100)\n",
    "    for i in range(2,Epoch_S+1):\n",
    "        if  int(P.history['accuracy'][-i]*100)== Before:\n",
    "            C=C+1\n",
    "        else:\n",
    "            C=1\n",
    "        Before=int(P.history['accuracy'][-i]*100)\n",
    "        print(Before)\n",
    "    if C==Epoch_S:\n",
    "        break\n",
    "    P = siamese_net.fit([l, r], lbls, epochs = Epoch_S, shuffle=True, batch_size=128, callbacks=history)\n",
    "print(j+1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "id": "QVF4gne7y52D"
   },
   "source": [
    "### Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hidden": true,
    "id": "vOrTcoNLy52F"
   },
   "outputs": [],
   "source": [
    "valid_ds = {}\n",
    "\n",
    "for i, task in enumerate(tox21_tasks_test):\n",
    "    temp = []\n",
    "    for j , data in enumerate(valid_dataset):\n",
    "        smiles, embbed_drug, onehot_task, embbed_task, lbl, task_name = data\n",
    "        if task ==  task_name:\n",
    "            temp.append(data)\n",
    "    \n",
    "    valid_ds[task] = temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "hidden": true,
    "id": "ZjQ0tZSJy52G",
    "outputId": "134a0ef5-bcb8-466e-ed15-7d508bf4faf6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SR-HSE : \n",
      "203/203 [==============================] - 0s 784us/step - loss: 0.1890 - accuracy: 0.9394 - mae: 0.1164 - mse: 0.0499 - auc_2: 0.8020\n",
      "SR-MMP : \n",
      "182/182 [==============================] - 0s 753us/step - loss: 0.3250 - accuracy: 0.8642 - mae: 0.1974 - mse: 0.0994 - auc_2: 0.8466\n",
      "SR-p53 : \n",
      "212/212 [==============================] - 0s 732us/step - loss: 0.1863 - accuracy: 0.9377 - mae: 0.1156 - mse: 0.0504 - auc_2: 0.8412\n"
     ]
    }
   ],
   "source": [
    "task_scores = [tox21_tasks_test for tox21_tasks_test in range(len(tox21_tasks_test))]\n",
    "\n",
    "for i, task in enumerate(tox21_tasks_test):\n",
    "    print(task,\": \")\n",
    "\n",
    "    y_test = []\n",
    "    l_val = []\n",
    "    r_val = []\n",
    "    lbls_valid = []\n",
    "    for data in valid_ds[task]:\n",
    "       \n",
    "        smiles, embbed_drug, onehot_task, embbed_task, lbl, task_name = data\n",
    "        l_val.append(embbed_drug[0])\n",
    "        r_val.append(embbed_task)\n",
    "        lbls_valid.append(lbl)\n",
    "            \n",
    "    l1 = np.asarray(l_val)\n",
    "    r1 = np.asarray(r_val)\n",
    "    lbls_valid = np.asarray(lbls_valid)\n",
    "\n",
    "    score = siamese_net.evaluate([l1,r1],lbls_valid)\n",
    " \n",
    "    result =(score[1], score[4])\n",
    "    task_scores[i] = task,result\n"
   ]
  }
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